{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Mayank_work.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "SB4DjtLwIDkq",
        "colab_type": "code",
        "outputId": "77cd715a-a234-44f5-be72-b394fc2698f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
        "from keras.layers.convolutional import Convolution3D, MaxPooling3D\n",
        "from keras.optimizers import SGD, RMSprop\n",
        "from keras.utils import np_utils, generic_utils\n",
        "import theano\n",
        "import os\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import cv2\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_validate\n",
        "from sklearn import preprocessing\n",
        "from keras import backend as K\n",
        "K.set_image_data_format('channels_first')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lt1Ju6i1LOAm",
        "colab_type": "code",
        "outputId": "eba2a2b0-a2e6-4d79-cf31-be39f129d619",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UxQbTtcLOOt",
        "colab_type": "code",
        "outputId": "ca39c52c-1517-4ae1-80a5-2dd425f25ad0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd /content/gdrive/My\\ Drive/"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AMF3XWnhLBi4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# image specification\n",
        "img_rows,img_cols,img_depth=80,60,9\n",
        "\n",
        "\n",
        "# Training data\n",
        "\n",
        "X_tr=[]           # variable to store entire dataset\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-iw-hr66Vdm4",
        "colab_type": "code",
        "outputId": "c262bc4d-f7cd-47a3-c72f-c024115b50eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "path = \"dataset/kth dataset\"\n",
        "folders = os.listdir(path)\n",
        "for folder in folders:\n",
        "  filepath = os.path.join(path, folder)\n",
        "  files = os.listdir(filepath)\n",
        "  print(len(files))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100\n",
            "100\n",
            "100\n",
            "100\n",
            "99\n",
            "100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Bbl8pf-P-0d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path = \"dataset/kth dataset\"\n",
        "folders = os.listdir(path)\n",
        "for folder in folders:\n",
        "  filepath = os.path.join(path, folder)\n",
        "  files = os.listdir(filepath)\n",
        "  for file in files:\n",
        "    vid = os.path.join(path, folder, file)\n",
        "    frames1 = []\n",
        "    frames2 = []\n",
        "    frames3 = []\n",
        "    optflow = np.zeros((img_depth-1, img_rows, img_cols, 2))\n",
        "    optflow_x = np.zeros((img_depth-1, img_rows, img_cols, 2))\n",
        "    optflow_y = np.zeros((img_depth-1, img_rows, img_cols, 2))\n",
        "    cap = cv2.VideoCapture(vid)\n",
        "    fps = cap.get(5)\n",
        "    # print (\"Frames per second using video.get(cv2.cv.CV_CAP_PROP_FPS): {0}\".format(fps))\n",
        "    for k in range(img_depth):\n",
        "        ret, frame = cap.read()\n",
        "        frame=cv2.resize(frame,(img_rows,img_cols),interpolation=cv2.INTER_AREA)\n",
        "        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
        "        gradient_x = cv2.Sobel(gray,cv2.CV_64F,1,0,ksize=5)\n",
        "        gradient_y = cv2.Sobel(gray,cv2.CV_64F,0,1,ksize=5)\n",
        "        if k+1<img_depth:\n",
        "          optflow[k] = cv2.calcOpticalFlowFarneback(gray[k],gray[k+1], None, 0.5, 3, 15, 3, 5, 1.2, 0)\n",
        "        \n",
        "        frames1.append(gray)\n",
        "        frames2.append(gradient_x)\n",
        "        frames3.append(gradient_y)\n",
        "        #plt.imshow(gray, cmap = plt.get_cmap('gray'))\n",
        "        #plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis\n",
        "        #plt.show()\n",
        "        #cv2.imshow('frame',gray)\n",
        "\n",
        "        if cv2.waitKey(1) & 0xFF == ord('q'):\n",
        "            break\n",
        "    cap.release()\n",
        "    cv2.destroyAllWindows()\n",
        "    \n",
        "    optflow_x = cv2.normalize(optflow[...,0], None, 0, 255, cv2.NORM_MINMAX) \n",
        "    optflow_x = optflow_x.astype('uint8')  \n",
        "    optflow_y = cv2.normalize(optflow[...,1], None, 0, 255, cv2.NORM_MINMAX)\n",
        "    optflow_y = optflow_y.astype('uint8')\n",
        "    f1=np.array(frames1)\n",
        "    f2=np.array(frames2)\n",
        "    f3=np.array(frames3)\n",
        "    f4=np.array(optflow_x)\n",
        "    f5=np.array(optflow_y)\n",
        "    f4=np.reshape(f4,(img_depth-1,img_cols,img_rows))\n",
        "    f5=np.reshape(f5,(img_depth-1,img_cols,img_rows))\n",
        "\n",
        "    concat=np.concatenate((f1,f2,f3,f4,f5))\n",
        "    input=np.array(concat)\n",
        "    # print (input.shape)\n",
        "    ipt=np.rollaxis(np.rollaxis(input,2,0),2,0)\n",
        "    # print (ipt.shape)\n",
        "\n",
        "    X_tr.append(ipt)\n",
        "\n",
        "print(len(X_tr))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ylup3dKUmM2",
        "colab_type": "code",
        "outputId": "cc0bb271-811d-46ba-f145-056e198b68bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_tr_array = np.array(X_tr)\n",
        "X_tr_array.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(599, 60, 80, 43)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dsyKO5NXXlY5",
        "colab_type": "code",
        "outputId": "86ff41d3-818b-40a9-fa18-d3c8eeae4f10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_tr_array = np.array(X_tr)   # convert the frames read into array\n",
        "\n",
        "num_samples = len(X_tr_array)\n",
        "print (num_samples)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "599\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6psK73TOY2D0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Assign Label to each class\n",
        "\n",
        "label=np.ones((num_samples,),dtype = int)\n",
        "label[0:100]= 0\n",
        "label[100:200] = 1\n",
        "label[200:300] = 2\n",
        "label[300:400] = 3\n",
        "label[400:499]= 4\n",
        "label[499:] = 5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mz5w7mWHZJTr",
        "colab_type": "code",
        "outputId": "0b6bbd4e-7732-4f72-f685-bfaa2326636f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_tr_array=np.reshape(X_tr_array,(599,80,60,43))\n",
        "X_tr_array.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(599, 80, 60, 43)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h19-xIvPtVhc",
        "colab_type": "code",
        "outputId": "ecd63e48-535a-4f2d-8da5-3838b51cb280",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "train_data = [X_tr_array,label]\n",
        "\n",
        "(X_train, y_train) = (train_data[0],train_data[1])\n",
        "print('X_Train shape:', X_train.shape)\n",
        "\n",
        "train_set = np.zeros((num_samples, 1, img_rows,img_cols,43))\n",
        "\n",
        "for h in range(num_samples):\n",
        "    train_set[h][0][:][:][:]=X_train[h,:,:,:]\n",
        " \n",
        "\n",
        "patch_size = 43    # img_depth or number of frames used for each video\n",
        "\n",
        "print(train_set.shape, 'train samples')\n",
        "\n",
        "# CNN Training parameters\n",
        "\n",
        "batch_size = 2\n",
        "nb_classes =6 \n",
        "nb_epoch =50\n",
        "\n",
        "# convert class vectors to binary class matrices\n",
        "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
        "\n",
        "'''\n",
        "# number of convolutional filters to use at each layer\n",
        "nb_filters = [32, 32]\n",
        "\n",
        "# level of pooling to perform at each layer (POOL x POOL)\n",
        "nb_pool = [3, 3]\n",
        "\n",
        "# level of convolution to perform at each layer (CONV x CONV)\n",
        "nb_conv = [5,5]\n",
        "'''\n",
        "# Pre-processing\n",
        "\n",
        "train_set = train_set.astype('float32')\n",
        "\n",
        "train_set -= np.mean(train_set)\n",
        "\n",
        "train_set /=np.max(train_set)\n"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_Train shape: (599, 80, 60, 43)\n",
            "(599, 1, 80, 60, 43) train samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_IbRVMslZCwH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzLVMW-d7SQA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras import Input\n",
        "from keras.models import Model\n",
        "from keras.layers import Dense, Flatten, Conv3D, MaxPooling3D, Dropout, BatchNormalization\n",
        "from keras.layers.merge import concatenate\n",
        "from keras.utils import to_categorical\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import h5py"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lx314XPyqtac",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras.backend.tensorflow_backend as tfback"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZeY_BpgreLe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def _get_available_gpus():\n",
        "    \"\"\"Get a list of available gpu devices (formatted as strings).\n",
        "\n",
        "    # Returns\n",
        "        A list of available GPU devices.\n",
        "    \"\"\"\n",
        "    #global _LOCAL_DEVICES\n",
        "    if tfback._LOCAL_DEVICES is None:\n",
        "        devices = tf.config.list_logical_devices()\n",
        "        tfback._LOCAL_DEVICES = [x.name for x in devices]\n",
        "    return [x for x in tfback._LOCAL_DEVICES if 'device:gpu' in x.lower()]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_mkwV-M4rj4c",
        "colab_type": "code",
        "outputId": "f9a7fec3-e88b-422e-b207-d82015039bb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tfback._get_available_gpus = _get_available_gpus\n",
        "tfback._get_available_gpus()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/device:GPU:0']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDW7mp-drkYr",
        "colab_type": "code",
        "outputId": "911d1ad6-4fe9-466e-e3ea-ab2e475e204d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "tf.config.list_logical_devices()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[LogicalDevice(name='/device:CPU:0', device_type='CPU'),\n",
              " LogicalDevice(name='/device:XLA_CPU:0', device_type='XLA_CPU'),\n",
              " LogicalDevice(name='/device:XLA_GPU:0', device_type='XLA_GPU'),\n",
              " LogicalDevice(name='/device:GPU:0', device_type='GPU')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2wscChw7Xpi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        },
        "outputId": "0c75952e-dee3-42ee-e47e-9ee1954cb89e"
      },
      "source": [
        "# Define model\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Convolution3D(8, 9, 7, 3,input_shape=(1, img_rows, img_cols, 43), activation='relu'))\n",
        "model.add(MaxPooling3D(pool_size=(3, 3, 1)))\n",
        "model.add(BatchNormalization(center=True, scale=True))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Convolution3D(16, 7, 7, 3,input_shape=(1, img_rows, img_cols, 43), activation='relu'))\n",
        "model.add(MaxPooling3D(pool_size=(3, 3,1)))\n",
        "model.add(BatchNormalization(center=True, scale=True))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Convolution3D(32, 6, 4, 1,input_shape=(1, img_rows, img_cols, 43), activation='relu'))\n",
        "model.add(BatchNormalization(center=True, scale=True))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Flatten())\n",
        "#model.add(Dense(256, activation='relu', kernel_initializer='he_uniform'))\n",
        "model.add(Dense(nb_classes,init='normal'))\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', optimizer='RMSprop', metrics=['accuracy'])\n"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: UserWarning: Update your `Conv3D` call to the Keras 2 API: `Conv3D(8, (9, 7, 3), input_shape=(1, 80, 60..., activation=\"relu\")`\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: UserWarning: Update your `Conv3D` call to the Keras 2 API: `Conv3D(16, (7, 7, 3), input_shape=(1, 80, 60..., activation=\"relu\")`\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: UserWarning: Update your `Conv3D` call to the Keras 2 API: `Conv3D(32, (6, 4, 1), input_shape=(1, 80, 60..., activation=\"relu\")`\n",
            "  from ipykernel import kernelapp as app\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(6, kernel_initializer=\"normal\")`\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fgSQ7VJEogcI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#model.compile(loss='categorical_crossentropy', optimizer='RMSprop', metrics=['accuracy'])\n",
        "\n",
        "# Split the data\n",
        "\n",
        "X_train_new, X_val_new, y_train_new,y_val_new =  train_test_split(train_set, Y_train, test_size=0.2, random_state=4)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5YEWCt-G3lc0",
        "colab_type": "code",
        "outputId": "138735b8-3431-4e18-f77b-753d7fc0dc6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "hist = model.fit(X_train_new, y_train_new, validation_data=(X_val_new,y_val_new),batch_size=batch_size,nb_epoch = nb_epoch,shuffle=True)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 479 samples, validate on 120 samples\n",
            "Epoch 1/50\n",
            "479/479 [==============================] - 5s 11ms/step - loss: 2.2465 - accuracy: 0.3591 - val_loss: 1.4553 - val_accuracy: 0.4250\n",
            "Epoch 2/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 1.7541 - accuracy: 0.4301 - val_loss: 1.0279 - val_accuracy: 0.6083\n",
            "Epoch 3/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 1.5969 - accuracy: 0.4656 - val_loss: 1.1163 - val_accuracy: 0.5667\n",
            "Epoch 4/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 1.3334 - accuracy: 0.4990 - val_loss: 1.0531 - val_accuracy: 0.5583\n",
            "Epoch 5/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 1.3816 - accuracy: 0.4906 - val_loss: 0.9538 - val_accuracy: 0.5500\n",
            "Epoch 6/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 1.0560 - accuracy: 0.5866 - val_loss: 0.9754 - val_accuracy: 0.5667\n",
            "Epoch 7/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 1.1294 - accuracy: 0.5386 - val_loss: 1.0005 - val_accuracy: 0.5833\n",
            "Epoch 8/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.9334 - accuracy: 0.5699 - val_loss: 1.1726 - val_accuracy: 0.5667\n",
            "Epoch 9/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.9863 - accuracy: 0.5783 - val_loss: 0.8420 - val_accuracy: 0.6250\n",
            "Epoch 10/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.8938 - accuracy: 0.6221 - val_loss: 0.8481 - val_accuracy: 0.6583\n",
            "Epoch 11/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.8207 - accuracy: 0.6493 - val_loss: 0.9457 - val_accuracy: 0.6000\n",
            "Epoch 12/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.8002 - accuracy: 0.6785 - val_loss: 0.9349 - val_accuracy: 0.6417\n",
            "Epoch 13/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.7775 - accuracy: 0.6660 - val_loss: 0.9834 - val_accuracy: 0.6333\n",
            "Epoch 14/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.7505 - accuracy: 0.6639 - val_loss: 0.9894 - val_accuracy: 0.6333\n",
            "Epoch 15/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.6794 - accuracy: 0.6994 - val_loss: 0.9204 - val_accuracy: 0.6500\n",
            "Epoch 16/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.6558 - accuracy: 0.7098 - val_loss: 0.8927 - val_accuracy: 0.6583\n",
            "Epoch 17/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.6734 - accuracy: 0.7182 - val_loss: 1.0987 - val_accuracy: 0.6000\n",
            "Epoch 18/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.6245 - accuracy: 0.7223 - val_loss: 0.9424 - val_accuracy: 0.6000\n",
            "Epoch 19/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.5847 - accuracy: 0.7453 - val_loss: 0.8004 - val_accuracy: 0.6583\n",
            "Epoch 20/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.5843 - accuracy: 0.7411 - val_loss: 0.8484 - val_accuracy: 0.6417\n",
            "Epoch 21/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.6036 - accuracy: 0.7203 - val_loss: 0.9525 - val_accuracy: 0.6583\n",
            "Epoch 22/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.5396 - accuracy: 0.7745 - val_loss: 0.8057 - val_accuracy: 0.7000\n",
            "Epoch 23/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.5352 - accuracy: 0.7745 - val_loss: 0.9602 - val_accuracy: 0.6667\n",
            "Epoch 24/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.4854 - accuracy: 0.8079 - val_loss: 0.9720 - val_accuracy: 0.6167\n",
            "Epoch 25/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.4968 - accuracy: 0.8100 - val_loss: 1.0346 - val_accuracy: 0.6500\n",
            "Epoch 26/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.5388 - accuracy: 0.7662 - val_loss: 0.9187 - val_accuracy: 0.6917\n",
            "Epoch 27/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.4867 - accuracy: 0.7933 - val_loss: 0.8870 - val_accuracy: 0.7083\n",
            "Epoch 28/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.4614 - accuracy: 0.7975 - val_loss: 0.9225 - val_accuracy: 0.6833\n",
            "Epoch 29/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.4229 - accuracy: 0.8058 - val_loss: 1.0441 - val_accuracy: 0.6500\n",
            "Epoch 30/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.4761 - accuracy: 0.7829 - val_loss: 1.2115 - val_accuracy: 0.6333\n",
            "Epoch 31/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.4206 - accuracy: 0.7996 - val_loss: 1.1173 - val_accuracy: 0.6750\n",
            "Epoch 32/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.3979 - accuracy: 0.8330 - val_loss: 1.2654 - val_accuracy: 0.6333\n",
            "Epoch 33/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.3936 - accuracy: 0.8351 - val_loss: 1.2370 - val_accuracy: 0.6417\n",
            "Epoch 34/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.3937 - accuracy: 0.8372 - val_loss: 1.2004 - val_accuracy: 0.6083\n",
            "Epoch 35/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.4176 - accuracy: 0.8351 - val_loss: 1.0449 - val_accuracy: 0.6583\n",
            "Epoch 36/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.4058 - accuracy: 0.8330 - val_loss: 1.0613 - val_accuracy: 0.7083\n",
            "Epoch 37/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.3440 - accuracy: 0.8476 - val_loss: 1.3567 - val_accuracy: 0.6250\n",
            "Epoch 38/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.3918 - accuracy: 0.8497 - val_loss: 1.0963 - val_accuracy: 0.6833\n",
            "Epoch 39/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.3688 - accuracy: 0.8497 - val_loss: 1.1820 - val_accuracy: 0.6750\n",
            "Epoch 40/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.3893 - accuracy: 0.8330 - val_loss: 1.1126 - val_accuracy: 0.6667\n",
            "Epoch 41/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.3401 - accuracy: 0.8476 - val_loss: 1.2780 - val_accuracy: 0.6833\n",
            "Epoch 42/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.3546 - accuracy: 0.8580 - val_loss: 1.3818 - val_accuracy: 0.6583\n",
            "Epoch 43/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.2775 - accuracy: 0.8935 - val_loss: 1.3095 - val_accuracy: 0.7083\n",
            "Epoch 44/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.3572 - accuracy: 0.8413 - val_loss: 1.2510 - val_accuracy: 0.7167\n",
            "Epoch 45/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.2990 - accuracy: 0.8810 - val_loss: 1.2765 - val_accuracy: 0.6750\n",
            "Epoch 46/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.2918 - accuracy: 0.8873 - val_loss: 1.4180 - val_accuracy: 0.6833\n",
            "Epoch 47/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.2549 - accuracy: 0.9040 - val_loss: 1.5317 - val_accuracy: 0.6417\n",
            "Epoch 48/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.2895 - accuracy: 0.8873 - val_loss: 1.4050 - val_accuracy: 0.6667\n",
            "Epoch 49/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.2634 - accuracy: 0.9248 - val_loss: 1.3979 - val_accuracy: 0.6833\n",
            "Epoch 50/50\n",
            "479/479 [==============================] - 4s 9ms/step - loss: 0.2862 - accuracy: 0.8873 - val_loss: 1.5378 - val_accuracy: 0.6750\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSYK6pl63fj7",
        "colab_type": "code",
        "outputId": "94801b3f-2f8d-470e-bcb1-5144c39d0d44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 884
        }
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "#hist = model.fit(train_set, Y_train, batch_size=batch_size,\n",
        "#         nb_epoch=nb_epoch,validation_split=0.2, show_accuracy=True,\n",
        "#           shuffle=True)\n",
        "#batch_size=32\n",
        "#nb_epoch=120\n",
        " # Evaluate the model\n",
        "score = model.evaluate(X_val_new, y_val_new, batch_size=batch_size)\n",
        "print('Test score:', score[0])\n",
        "print('Test accuracy:', score[1])\n",
        "\n",
        "\n",
        "# Plot the results\n",
        "train_loss=hist.history['loss']\n",
        "val_loss=hist.history['val_loss']\n",
        "train_acc=hist.history['accuracy']\n",
        "val_acc=hist.history['val_accuracy']\n",
        "xc=range(nb_epoch)\n",
        "\n",
        "plt.figure(1,figsize=(7,5))\n",
        "plt.plot(xc,train_loss)\n",
        "plt.plot(xc,val_loss)\n",
        "plt.xlabel('num of Epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.title('train_loss vs val_loss')\n",
        "plt.grid(True)\n",
        "plt.legend(['train','val'])\n",
        "print (plt.style.available) # use bmh, classic,ggplot for big pictures\n",
        "plt.style.use(['classic'])\n",
        "\n",
        "plt.figure(2,figsize=(7,5))\n",
        "plt.plot(xc,train_acc)\n",
        "plt.plot(xc,val_acc)\n",
        "plt.xlabel('num of Epochs')\n",
        "plt.ylabel('accuracy')\n",
        "plt.title('train_acc vs val_acc')\n",
        "plt.grid(True)\n",
        "plt.legend(['train','val'],loc=4)\n",
        "#print plt.style.available # use bmh, classic,ggplot for big pictures\n",
        "plt.style.use(['classic']) "
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "120/120 [==============================] - 0s 3ms/step\n",
            "Test score: 1.537817478478549\n",
            "Test accuracy: 0.675000011920929\n",
            "['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 560x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 560x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I3zyfLsgzYFM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}
